Maintaining Data Quality

Maintaining Data Quality

Maintaining Data Quality

Have you been underestimating data quality issue?

Data quality is often addressed in the form of employees or vendor-supplied consultants, accounting for 20-50% of the data warehouse projects, labor over at least a few weeks and, in some cases, several months, depending on project size and complexity. Data, most often, loses quality when integrated. Traditional integration techniques most often fail as data flows from multiple sources (built on different platforms) and in different formats. All data sources might not have effective information sharing mechanisms, which primarily makes integrated data unreliable.

Poor data quality is known to damage million dollars for any enterprise. Spending on implementing large CRM, BI or integration projects are a waste till the quality of data flowing to these systems remains low. In fact in the long run, bad data can lead to low customer satisfaction' and decreased customer retention. The three aspects critical to data are accuracy, consistency and timeliness. High quality data is dependent on these three standards. Inaccurate data is junk for any enterprise, thus, accuracy is important. Though all departments of an enterprise need data for different purposes, it is crucial for the entire enterprise to have consistent data. In 86% cases, low customer satisfaction is the result of obsolete customer data that may exist in several departments as redundant customer records. Duplicate customer records also increase the volume of databases. Data at the right time, for the right people determines the operational efficiency of any enterprise. It is the right data that becomes the basis for taking operational, tactical and strategic decisions.

The main factors that render low quality to data are:

Mismanaged data

Inadequate data acquisition process

Multiple data silos maintaining the same data

Redundant data across multiple channels

Ineffective data update process

It is important to identify the weak sources within an enterprise from where erroneous data flows. Over the years, enterprises tend to maintain data silos that keep expanding with faulty records. Due to disjointed network and insufficient integration capability, the data is further contaminated with erroneous information. Inaccurate, redundant and missing data continues to grow till it becomes an unmanageable set of fragmented and voluminous databases. Different systems are programmed to access data from the different faulty databases. CRM, ERP, BI and integration technologies that fail in enterprises are most often the result of substandard data. The data quality metrics are only the roadmap to improve the quality of data. Establishing a process to monitor results and set improvement goals is the next vital phase of data quality lifecycle. Cost, effort and time spent on the data quality process should be able to provide business benefits, accordingly. The improvement process is a set of business cases and specific areas that need to be addressed. With the improvement areas, the goals and the priority attached with each should be clearly identified. Personnel responsible for executing each improvement task should clearly understand the entire execution process. The improvement process can be taken as the yardstick to measure future data quality improvement. According to Gartner, through 2005, IT-driven data quality efforts will be largely ineffective in achieving improvement goals. For improvement efforts to succeed, senior management must recognize and adopt data quality as a key business priority.

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